code: def forward(
      ctx,
      input,
      offset,
      weight,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      deform_groups=1,
      bias=False,
      im2col_step=32,
  ):
      if input is not None and input.dim() != 4:
          raise ValueError(
              f"Expected 4D tensor as input, got {input.dim()}D tensor \
                    instead."
          )
      assert bias is False, "Only support bias is False."
      ctx.stride = _pair(stride)
      ctx.padding = _pair(padding)
      ctx.dilation = _pair(dilation)
      ctx.groups = groups
      ctx.deform_groups = deform_groups
      ctx.im2col_step = im2col_step
  
      # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
      # amp won't cast the type of model (float32), but "offset" is cast
      # to float16 by nn.Conv2d automatically, leading to the type
      # mismatch with input (when it is float32) or weight.
      # The flag for whether to use fp16 or amp is the type of "offset",
      # we cast weight and input to temporarily support fp16 and amp
      # whatever the pytorch version is.
      input = input.type_as(offset)
      weight = weight.type_as(input)
      ctx.save_for_backward(input, offset, weight)
  
      output = input.new_empty(
          DeformConv2dFunction._output_size(ctx, input, weight)
      )
  
      ctx.bufs_ = [
          input.new_empty(0),
          input.new_empty(0),
      ]  # columns, ones
  
      cur_im2col_step = min(ctx.im2col_step, input.size(0))
      assert (
          input.size(0) % cur_im2col_step
      ) == 0, "im2col step must divide batchsize"
      ext_module.deform_conv_forward(
          input,
          weight,
          offset,
          output,
          ctx.bufs_[0],
          ctx.bufs_[1],
          kW=weight.size(3),
          kH=weight.size(2),
          dW=ctx.stride[1],
          dH=ctx.stride[0],
          padW=ctx.padding[1],
          padH=ctx.padding[0],
          dilationW=ctx.dilation[1],
          dilationH=ctx.dilation[0],
          group=ctx.groups,
          deformable_group=ctx.deform_groups,
          im2col_step=cur_im2col_step,
      )
      return output
  
imports: from typing import Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair, _single
  from annotator.uniformer.mmcv.utils import deprecated_api_warning
  from ..cnn import CONV_LAYERS
  from ..utils import ext_loader, print_log
label_desc: Use image node_id 2 for calling the DeformConv2dFunction obj's underlying member method code with example usage: obj.forward(ctx, input, offset, weight, stride, padding, dilation, groups, deform_groups, bias, im2col_step) and returns: output